Name
Abstract | ||
|---|---|---|
In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IPDPS.2017.55 | 2017 31ST IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS) |
Keywords | Field | DocType |
Solid State Disk, Internal Parallelism, Workload Characteristics, Garbage Collection, I/O Scheduler | Computer science,Parallel computing,Parallel processing,Response time,Communication channel,Exploit,Input/output,Garbage collection,Cognitive neuroscience of visual object recognition,Distributed computing | Conference |
ISSN | Citations | PageRank |
1530-2075 | 4 | 0.37 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiayang Guo | 1 | 9 | 1.62 |
| Yiming Hu | 2 | 639 | 44.91 |
| Bo Mao | 3 | 163 | 19.27 |
| Suzhen Wu | 4 | 283 | 23.14 |